Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw

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https://doi.org/10.1007/978-0-306-48368-4ficulties in obtaining training data and the absence of color images. Thus, overall performance heavily depends on individual human skill in tuning hundreds of parameters. This paper presents a defect inspection technique using a defect probability image (DPI) and a deep convolutional neural network

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Fusing Solar and Stellar Cosmologies,roducing the ability to reason about objects at the level of their constituent parts. While classic retrieval systems can only formulate simple searches such as . our new approach can formulate advanced and semantically meaningful search queries such as: .. Many applications could benefit from such

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Contrasting Competitive Plausibility,s using deep convolutional neural networks (CNN). Face detection is implemented with region-based framework as previous work like Faster RCNN. We model the pose estimation as a classification and regression problem: first divide continuous head poses into several discrete clusters, then adjust poses

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What Made the Renaissance in Europe?,om unlabeled data. In this paper, we propose an unsupervised deep learning framework to provide a potential solution for the problem that existing deep learning techniques require large labeled data sets for completing the training process. Our proposed introduces a new principle of joint learning o

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The Alhazen Optical Revolution,the affine parameters consistent with a pre-estimated epipolar geometry from the point coordinates and the scales and rotations which the feature detector obtains. The closed-form solution is given as the roots of a quadratic polynomial equation, thus having two possible real candidates and fast pro

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